Students’ Perceptions of Effective EFL Teachers in University Settings in Cyprus
Bibliographic record
Abstract
This study sought to identify what characteristics and teaching behaviours describe effective EFL University teachers as perceived by Cypriot students. Data were collected by means of a questionnaire and focus group interviews. Findings have provided evidence that effective language teaching seems to be related to a more learner-centred approach to language learning and teaching, which, in turn, assumes a more assisting, mediating role for the language teacher. According to the participants of this study, an effective EFL teacher is no longer considered one who has a directive and authoritarian role in the learning process but one who takes into consideration his/her students’ individual differences, language anxiety, abilities and interests and design learning environments accordingly. Language teachers’ skills in using technology and engaging students in meaningful classroom interactions by involving them in group tasks designed around real life topics and authentic language use have also been emphasised. Participants’ views call for EFL teachers in university settings to move beyond the traditional focus-on-form approach to language teaching which views language learning as an individual activity, to the adoption of the communicative approach to language teaching which acknowledges the social aspect of learning and as such, it depends upon meaningful interactions with peers. EFL teachers working in tertiary education should use these findings as a yardstick to better understand themselves and the needs of their students for the enhancement of the learning process.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".